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May 2022 DOI 10.14302/issn.2379-7835.ijn-22-4190
Bartle JodieCorresponding author
Department of Nutrition & Food Services, Royal Children’s Hospital, Melbourne.
Background Nutrition support during the acute phase post allogeneic haematopoietic stem cell transplantation (HSCT) is required to optimise short- and long-term outcomes for children. An algorithm was developed and evaluated to assist clinicians to make objective and consistent enteral feeding decisions. Methods The algorithm was evaluated on all patients who underwent allogeneic HSCT treatment between November 2017 - February 2019. Results Of the 48 patients, 43 had a nasogastric tube (NGT) inserted, of which 36 patients received a hydrolysed peptide-based formula, 5 patients received a whole protein formula only and 2 patients were fed an amino acid-based formula. Parenteral nutrition (PN) was used in 41 of the patients. Eleven did not have an NGT in-situ at the commencement of HSCT. Of the remaining 37 patients, 26 followed the algorithm and 11 patients did not comply. The group of patients who did not follow the algorithm had the longest median length of stay (LOS) of 49 days. Patients receiving only EN had the lowest median LOS of 30 days. The two groups that reported better weight outcomes were those who followed the algorithm and those who were fully EN fed. Conclusions Effective use of the HSCT feeding algorithm indicated improved patient outcomes for children undergoing HSCT, with better weight outcomes and reduced LOS. Recommendations to improve the efficacy and compliance of the algorithm include regular education/input to the oncology medical teams to better understand objective thresholds for EN and PN commencement.
Jun 2021 DOI 10.14302/issn.2766-8681.jcsr-21-3796
Raj StanleyCorresponding author
Department of Physics, Loyola College, Chennai, Tamil Nadu-India
Geoelectrical resistivity data collected from the ground contain lot of noises and errors. It requires efficient algorithm to reduce the errors to make an actual inversion models. Though different algorithm can be applied, nature inspired algorithm is more potential in inverting geoelectrical data in an elegant and comprehensive way. Bargain Optimization (BO) algorithm is framed on the concept of bargaining things to purchase for needs. In general, effective bargaining results in more profit and leads to loss when it fails. In this research work, Bargain Optimization algorithm is applied to invert geoelectrical data and the effective bargaining will take time to process and to obtain the required model. The input data is AB/2, apparent resistivity data and the inverted model through BO algorithm is successfully matched with the available litholog section of the study area. The output graphs have profit/loss bar graph, which reveals the status of bargaining during a particular number of epochs.
Apr 2021 DOI 10.14302/issn.2692-1537.ijcv-21-3804
Isea RaúlCorresponding author
Fundación Instituto de Estudios Avanzados, Hoyo de la Puerta, Baruta, Venezuela.
An algorithm to determine the possible mutations that can occur in the S protein responsible of the Covid-19 in humans is designed. To do that, nine tridimensional sequences available in the Protein Data Bank similar to the initial strain sequenced in Wuhan (December 2019) are identified. The conditions driving this potential mutation are: (1) an accumulated number of mutations greater than (or equal to) 5 in each position; (2), a cumulative value of the different variations of Gibbs free energy less than -2.0 Kcal/mol; and (3), a squared fluctuation greater than 1.6 Å obtained according to calculations for normal mode analysis based on anisotropic network models (ANM) after averaging the first 20 vibration modes. The result is that 491 positions can mutate, while 424 positions did not provide any mutation. Finally, the results reveal that there are mutations that cannot be predicted, so more studies are needed to determine why they are present in the human population.
Jul 2020 DOI 10.14302/issn.2643-2811.jmbr-20-3449
Stanley Raj A.Corresponding author
Department of Physics, Loyola College, Chennai, Tamilnadu- 600034, India.
Electrical resistivity method is often used to estimate the subsurface structure of the earth. Many inversion algorithms are available to estimate the subsurface features. However, predicting the exact parameter in the non-linear subsurface of the earth is difficult because of its complex composition. Soft computing tools can approximate the subsurface parameters more clearly. Each soft computing tool has certain advantages and disadvantages. A hybrid formation of algorithms will make the decision more appropriate than depending on a single tool. Here in our study the data obtained through Vertical Electrical Sounding has been used to determine the sub surface characteristics of earth viz., true resistivity and thickness. Artificial Neural Networks (ANN) requires certain optimizing procedures. Here in this paper, Genetic Algorithm (GA) is applied to optimize Artificial Neural Networks (ANN). This coupled approach is tested with the field data. Error percentage of algorithm nearly mimics the behavior of earth and is verified. The best performance result shows that this technique can be implemented to estimate the non-linear characteristics of the earth more noticeably.
Apr 2020 DOI 10.14302/issn.2641-5526.jmid-20-3302
Y.FakirCorresponding author
Laboratory of Information Processing and Decision Support, Faculty of Sciences and Technics, Sultane Moulay Slimane University, Beni Mellal, Morocco
Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model
Apr 2026 DOI 10.14302/issn.2574-450X.jom-26-6138
Dahlmann NicolausCorresponding author
Indices, based on data such as height and weight in general and in particularly the body mass index (BMI), are often used to assess overweight. However, there is limited capacity to differentiate the amount of fat mass between individuals. This review refers to an anthropometric model called Dahlmann-Body-Analysis (DBA), which uses simple anthropometric parameters to define a Reference Weight (Ref-Wt). It is based on hand circumference as a proxy for the skeletal frame and, in addition, the circumference of the abdomen as a proxy for central obesity. Processed through a network of algorithms, the DBA model enabled to differentiate the Difference Weight – that means the difference between the Actual Weight and the Reference Weight – into fat mass and skeletal muscle mass. The DBA-model resembles the 2-component model of Albert R. Behnke, which he considered as a living functional construct including essential fat. The DBA-model matches with Behnke`s 2-component model insofar, as the essential fat is replaced by a physiological amount of fat tissue. The review summarizes studies to compare DBA-derived data with Metropolitan Life Insurance tables, evaluates DBA-derived fat tissue mass with bioelectrical impedance analysis (BIA) derived results and analyses the meaning of the DBA model in clinical settings to uncover the underlying mechanisms of metabolic syndrome (MetS) pathogenesis with increasing amounts of fat mass. The model offers the opportunity to calculate changes in fat or muscle tissue in an absolute (kg) or relative (%) amount on individuals. The data suggest that the DBA-model has satisfactory prediction qualities for use as a practical tool in public health care.
Jul 2025 DOI 10.14302/issn.2766-8681.jcsr-25-5618
Isea RaulCorresponding author
Humanity is persistently threatened by global pandemics- exemplified by the Black plague, the Spanish flu, and COVID-19, which reveal a continual absence of concern in real-time prevention. To forecast biological threats in the future and spur proactive human response, the term Global Biological Consciousness (GBC) is introduced.GBC requires an Extended Bioethics, a dynamic ethical framework for conscious management mediated by GBC. This perspective will enable preventive actions and will seek global biological resilience through the algorithmic responsibility of AI and systemic justice, as will be explained in the work. The GBC, through Extended Bioethics, will provide an ability to analyze biological data as it occurs using AI and quantum computing, expect outbreaks before they happen and attenuate their effects, here creates a new ethical contract for all humankind as they co-exist in a biological world.
May 2024 DOI 10.14302/issn.2998-1506.jpa-24-5058
Shrestha SwatiCorresponding author
Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.
Oct 2023 DOI 10.14302/issn.2768-0207.jbr-23-4753
Isea RaúlCorresponding author
The rapid growth of data and scientific journals has led to the promotion of data-based hypotheses. Data-driven hypotheses can also be used to establish new scientific laws or confirm existing ones, demonstrating the foundation of this philosophy. To introduce this idea, this article presents a Python-based computational algorithm that can generate system dynamics equations without using working hypotheses.
Jul 2023 DOI 10.14302/issn.2639-3166.jar-23-4648
Masoero GiorgioCorresponding author
The purpose of the trial was to check the effects of two grapevine treatments on the plant activity and on the bioactivity and biovariability of the soil. An alkaline complex of Soluble Biobased Substances (SBS) was used in soil at 30 g per plant in a single solution. Salicylic Acid (SA) was used on leaves at 150 mg l-1 every two weeks at 50 ml plant. The plants were examined for their foliar pH and NIR spectra. The soil bioactivity was monitored by means of hay-Litterbag-NIRS (LBN) in combination with the Teabag Index (TBI), using rooibos and green tea that had been buried for 60 days. The evolution of the TBI presented here concerns the TBI-NIRS spectroscopic method used for discriminant analysis. A new algorithm was used to estimate the soil microbiome from the green Teabag spectra. The obtained results showed that the plants and the soil responded to the treatments. In fact, SBS, but not SA, lowered the leaf pH by 5%, an unexpected and original result. Both treatments increased the variability of the leaf composition, with a lower discrimination, based on the NIR spectra, from the Control (75%) to 44% (SA) and 38% (SBS). The TBI method, which is based on weights, was less efficient (67%) than the TBI-NIRS of rooibos (96%) or the LBN of hay (80%), but it was like the TBI-NIRS of green tea (74%). The LBN analyses indicated that the mycorrhizal index had increased by 8% in SA but had reduced by 7% in SBS, while both treatments reduced the activity of the microbes, which did not affect the soil respiration rate. The mineral N in the soil was substantially raised by about 11÷69%. The Taxa profiles showed marked deviations from the Control. Moreover, the SBS treatment reduced the Glomeromycota by 35%, which matched to the reduction in the mycorrhizal index. The most favored Bacteria from the treatments were Proteobateriaand Actinobacteria, with Mortierellomycota being the most penalized. Neither treatment affected the production, but both delayed the technological maturity by 9-11%, while the SBS retarded the phenolic maturity by about 18%. It has been concluded that a simple treatment of vines can affect the bioactivity in the leaves and berries as well as the biovariability of the soil.
Mar 2023
Oleg BodinCorresponding author
The visual presentation of the results of the analysis of cardio graphic information is an important element of the diagnostic process. The article discusses the issues of visualization of the state of the heart, notes the importance of a visual representation of the processes of the functioning of the heart for diagnosis. The Delaunay triangulation is considered and it is proved that on its basis it is possible to approximate an arbitrarily complex surface. An algorithm for constructing a surface model of the heart based on Delaunay triangulation has been developed. The issues of texture generation are covered in detail. The texture is a convenient tool for displaying the state ofthe heart, as it allows you to visually show the place of possible damage on the surface of the heart.
Mar 2023 DOI 10.14302/issn.2768-0207.jbr-23-4478
FAKIR YoussefCorresponding author
Spatial data mining (SDM) is searching important relationships and characteristics that can clearly exist in spatial databases. This content aims to compare object clustering algorithms for spatial data mining, before identifying the most efficient algorithm. To this end, this paper compare k-means, Partionning Around Medoids (PAM) and Clustering Large Applications based on RANdomized Search (CLARANS) algorithms based on computing time. Experimental results indicate that, CLARANS is very efficient and effective.
Mar 2021 DOI 10.14302/issn.2768-0207.jbr-21-3455
FAKIR YoussefCorresponding author
Laboratory of Information Processing and Decision Support, University Sulan Moulay Slimane
In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequential algorithm (GSP) and Sequential PAttern Discovery using Equivalence classes (SPADE). These two algorithms are based on the Apriori algorithms. Experimental results have shown that SPADE consumes less time than GSP algorithm.
Dec 2020 DOI 10.14302/issn.2641-4538.jphi-20-3641
Turk TahirCorresponding author
Background Evidence based message design and efficient dissemination of messages are critical to the success of tobacco control mass media campaigns. Although evidence to measure effectiveness of messages is emerging within low -and middle-income country (LMIC) settings, evidence-based approaches for mass media message dissemination is currently lacking due to challenges in accurate assessment of gross rating points (GRPs) for efficient delivery of campaign messages. Approaches to more accurately predict optimal campaign impact are required to achieve best-buys in resource constrained settings Method A case study approach compared findings from two national tobacco control mass media campaigns implemented in Bangladesh. Stage one reviewed protocols to assess the efficacy of message designs. Second stage analysis involved a review of the mass media campaign recall findings from cross-sectional, post-intervention surveys. Last, a post assessment of GRPs for both campaigns was conducted to support the development of an algorithm to better predict campaign impact at the greatest cost-efficiencies. Results Message mean pre-test scores identified that the Baby Alive campaign scored approximately 20% lower than mean pre-test scores of messages for the Graphic Health Warning campaign. Media dissemination for the Baby Alive campaign was also relatively low at 165GRPs achieving 16.8% prompted recall while the Graphic Health Warning campaign delivered 292GRPs to achieve 47.0% prompted recall. The analytic-predictive model identified that for messages with high pre-test scores an increase of only 1.5GRPs was required to the existing media plan to potentially achieve an additional percentage point of recall. Discussion Given the weaknesses in GRP calculations in LMIC settings, analysis of multiple metrics should be considered to achieve best buys for tobacco control mass media campaigns. Based on optimal message mean pre-test scores of 90%+ and delivery of 292GRPs, which achieved 47% campaign recall, optimal recall of 70% could be predicted with a media plan delivering 342GRPs. More analytical-predictive mass media programming models need to be developed in other LMIC settings examining multiple campaign findings to confirm if this algorithm can provide better returns on investment with efforts directed toward delivering interventions that are supported by a strong evidence base.
Jul 2020 DOI 10.14302/issn.2641-5526.jmid-20-3424
Fakir YoussefCorresponding author
Faculy of Sciences and Technics, Sultan Moulay Slimane University, Morocco
In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting & intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.
Apr 2020
Stanley Raj A.Corresponding author
Department of Physics, Loyola College, Chennai, Tamilnadu- 600034, India.
Geoelectrical resistivity data is used for estimating the subsurface features of earth. It is very difficult to estimate the depth and true resistivity analytically, therefore many mathematical models approximates the result. The approximation relies on many parameters as the heterogenous model of earth is difficult to map. Conventional interpretation algorithm mostly uses the forward modelling technique which is limited for different lithologies. Here we presented ResinvANFIS v1.0 software platform to invert any type (A, Q, K, H or any mixed data types) of resistivity data having AB/2 and apparent resistivity data as input. This kind of generalised platform has not been done elsewhere to invert data directly using soft computing approach.
Apr 2020 DOI 10.14302/issn.2691-8862.jvat-20-3278
Isea RaúlCorresponding author
Fundación Instituto de Estudios Avanzados, Hoyo de la Puerta, Baruta. Venezuela.
The goal of this paper is to obtain the numerical consensus of B cell epitopes from the three-dimensional structure of the prefusion spike glycoprotein of the new betacoronavirus that could lead to the development of a vaccine to 2019-nCoV. In order to do that, we first calculated the B-cell epitopes that are predicted using fourteen different mathematical algorithms. Later, we obtained the consensus of B-cell epitopes according to the Similarity Index, and finally selecting the best candidates according to the results of a function called <F> which is evaluated for the glycoprotein. The best candidates that we obtained in order to design a vaccine are SSANNCT, PLQSYGFQPT, TESNKKFLP, NNSYEC, AENS, LPDPSK and YDPLQPE.
Jan 2020 DOI 10.14302/issn.2689-4602.jes-19-3155
Mikhailovsky GeorgeCorresponding author
Global Mind Share, 878 W Ocean View Ave., Norfolk, VA, 23503, USA
As shown earlier, the algorithmic complexity, like Shannon information and Boltzmann entropy, tends to increase in accordance with the general law of complification. However, the algorithmic complexity of most material systems does not reach its maximum, i.e. chaotic state, due to the various laws of nature that create certain structures. The complexity of such structures is very different from the algorithmic complexity, and we intuitively feel that its maximal value should be somewhere between order and chaos. I propose a formula for calculation such structural complexity, which can be called - structuredness. The structuredness of any material system is determined by structures of three main types: stable, dissipative, and post-dissipative. The latter are defined as stable structures created by dissipative ones, directly or indirectly. Post-dissipative structures, as well as stable, can exist for an unlimited time, but at the micro level only, without energy influx. The appearance of such structures leads to the “ratchet” process, which determines the structure genesis in non-living and, especially, in living systems. This process allows systems with post-dissipative structures to develop in the direction of maximum structuring due to the gradual accumulation of these structures, even when such structuring contradicts the general law of complification.
Dec 2019 DOI 10.14302/issn.2690-6759.jpar-19-3081
Ibrahim SangaréCorresponding author
Institut Supérieur des Sciences de la Santé, Université Nazi BONI, Bobo-Dioulasso, Burkina Faso
Malaria and typhoid fever are two endemic infectious diseases in developing tropical countries including Burkina Faso. There are two distinct infectious diseases with many similar clinical signs. In each sanitary area, it is important to describe the "typhomalaria" epidemiology to elaborate adequate diagnosis algorithm and efficient treatment protocol. A cross-sectional study was carried out from July to October 2014 in the lab department of University Hospital Souro SANOU, Bobo-Dioulasso. All microscopy positive malaria during the study period was included. Serodiagnosis of Widal and Felix was performed systematically in all Plasmodium spmalaria cases. Titers of antibodies anti-agglutinin O equal or higher than 1/400 and/or 1/800 for anti-agglutinin H antibodies were considered positive for Salmonella sp. A total of 283 malaria cases were included in this study, majority falciparum malaria. In this malaria cases, 91 patients were seropositive for Salmonella sp. "Typhomalaria" co-infection prevalence was 34.3% (CI 95% (28.8%; 40.1%)). The patient with the normal hemoglobin rate had the highest prevalence of co-infection (46.7% versus 30.9; p=0.02). Malaria and typhoid fever co-infection was high (approximately 1/3 of malaria cases) in University hospital of Bobo-Dioulasso. This study revealed the need to explore typhoid fever in malaria confirmed cases, especially in persistent fevers and non-anemic situation despite adapting antimalarial treatment.
Jun 2019 DOI 10.14302/issn.2639-3166.jar-19-2785
A. Mari NicolásCorresponding author
Instituto Nacional de Tecnología Agropecuaria – Agencia de Extensión Rural Cruz del Eje
In Córdoba, Argentina, the peri-urban horticulture is in conflict with industrial agriculture and urban development. This problem is partly due to urban expansion to rural areas occurred in the last years and to monoculture farming, which has replaced traditional fruit and vegetable cropping in the region. This transformation process has raised concern about the current and future availability of productive sectors that can sustain food supply within the city boundaries and its immediate surroundings as well as about the loss of ecosystem services associated with peri-urban natural environments. Although these dynamic processes are well known, they have not been described or quantified in Córdoba. Baseline information about land use and its dynamics in productive areas or about number of producers is insufficient and/or out of date. At O-AUPA (Spanish acronym for Observatory of Urban and Peri-urban Agriculture and Agroecology) different mapping strategies are developed to contribute to the understanding of the land dynamics in the Green Belt of Córdoba (GBC) and the rural environments surrounding the city. In this work, we present a method based on the use of remote sensing and geographical information systems to characterize urban, peri-urban and rural areas of Córdoba city with the aim of evaluating the temporal dynamics of urban growth and the current state of land use and cover. We mapped and quantified the urban growth between 1974 and 2014, and evaluated land use in peri-urban and rural areas in 2015. We used satellite information from Landsat TM 5 to map the urban growth via a principal component analysis (PCA) and SPOT 5 imagery to characterize the current land use and land cover with the support vector machine classification algorithm. The results show an urban area growth of 46.5% over almost 40 years within the boundaries of the Capital department. Farm plot size increased, showing a concentration of land ownership, implying a reduced number of producers. Evidence indicates the importance of defining land planning guidelines that limit the advance of the urban frontier to valuable agricultural systems, ensure diversification of productive activities and protect and develop the fresh food production systems at the local level.
May 2019 DOI 10.14302/issn.2639-3166.jar-19-2780
Masoero GiorgioCorresponding author
Accademia di Agricoltura di Torino, Italy
The inoculation of soil with a bio-fertilizer (BF), with arbuscular mycorrhiza fungi, characterizes a Symbiotic (S) agriculture mode, aimed at promoting the yield and health of crops through modifications in the rhizosphere as well as in the plant phenotype. The main objective of this study was to reduce the incidence of Olive Quick Decline Syndrome (OQDS, involving Xylella fastidiosasubsp.pauca) that afflicts the olive groves in Apulia (Italy). Non-inoculated control (C) plants were compared with Symbiotic (S) plants inoculated with 20 kg ha-1 of Micosat F ®, through a 15 cm deep scarification, in the groves of seven farms covering an area of 27 ha. In addition to a visual observation of 484 plants, to obtain a gradation of the disease severity, some objective rapid type methods were utilized to survey the plants and soil , namely leaf pH, NIR tomoscopy of the leaves, hay-litter-bag probes coupled with NIR spectroscopy and the prediction of soil induced respiration. The fingerprinting of the S and C types of leaves and litter-bags was ascertained by means of the use of a random forest algorithm in the classification matrices. The results on the symptoms appeared variable: they were significantly mitigated in two groves out of six, but they were aggravated in one. All the rapid measurements became essentials in a “holistic” model which was able to explain over 95% of the average mitigation / null / aggravation response to BF inoculation. The holistic model gathers differential and compositional analyses of the leaf (pH, crude protein, water) and of the soil (respiration), but depends mainly on the fingerprinting of the C and S leaves and litter-bags. Two keys were identified for a successful inoculation: a high degree of variability of the soil conditions permitting hospitality for the BF with enhancement of the microbial activity in the S soil (lowering the fingerprint of the control litter-bags) and homogeneity of the leaves (with increases in the fingerprint of the S leaves treated with BF). In short, the inoculation of diseased plants with one BF consortium is far from being the ultimate remedy to mitigate OQDS in all situations. Further studies are needed, at a field level, to clarify the soil hosting capacity and to define the mycorrhizal and / or endophytic * plant * pathogen interactions, even using rapid methods.
Jan 2019 DOI 10.14302/issn.2641-5526.jmid-18-2529
Luis Fernández-Martínez JuanCorresponding author
Group of Inverse Problems, Optimization and Machine Learning. Department of Mathematics. C/ Federico García Lorca, 18. 33007 Oviedo. University of Oviedo. Spain
Discrimination of case-control status based on gene expression differences has potential to identify novel pathways relevant to neurodegenerative diseases including Parkinson’s disease (PD). In this paper we applied two different novel algorithms to predict dysregulated pathways of gene expression across several different regions of the brain in PD and controls. The Fisher’s ratio sampler uses the Fisher’s ratio of the most discriminatory genes as prior probability distribution to sample the genetic networks and their likelihood (accuracy) was established via Leave-One-Out-Cross Validation (LOOCV). The holdout sampler finds the minimum-scale signatures corresponding to different random holdouts, establishing their likelihood using the validation dataset in each holdout. Phenotype prediction problems have by genesis a very high underdetermined character. We used both approaches to sample different lists of genes that optimally discriminate PD from controls and subsequently used gene ontology to identify pathways affected by disease. Both algorithms identified common pathways of Insulin signaling, FOXA1 Transcription Factor Network, HIF-1 Signaling, p53 Signaling and Chromatin Regulation/Acetylation. This analysis provides new therapeutic targets to treat PD.
Nov 2018 DOI 10.14302/issn.2769-2264.jw-18-2393
Bireslavskii E.N.Corresponding author
Department of Applied Mathematics and Informatics, University of Civil Aviation, St. Petersburg, Russia
We consider a plane steady-state filtration in a rectangular bridge with a partially impermeable vertical wall in the presence of evaporation from a free surface of groundwater. To study the effect of evaporation, a mixed multiparametric boundary-value problem of the theory of analytic functions is formulated and using the method of P. Y. Polubarinova-Kochina. Based on the proposed model, an algorithm is developed to calculate the dependence of efficiency and productivity of hydrodynamic analysis.
May 2018 DOI 10.14302/issn.2638-4469.japb-18-2127
W. Korn RobertCorresponding author
Department of Biology, Bellarmine University, Louisville Ky. 40205, USA
Four rules for good anatomical modeling of plants are explored. First, the cell is the reference source for modelling at any level. Second, developmental signaling occurs between few cells, about 12. Third, rules of are algorithmic and not simply physical forces as proposed by Thompson. Finally, it is desirable to proposed a likely alternative model that can be discounted. The main value of modelling is selecting data for modelling rather than the by a biased investigator.
Apr 2018 DOI 10.14302/issn.2768-0207.jbr-17-1925
Bai QifengCorresponding author
Key Lab of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Big data research has become popular and exciting studies in almost all scientific fields such as biology, chemistry, epidemiology, medicine and drug discovery. The various systems and platforms produce large amounts of data every day. It will be very helpful for the researchers and workers to deal with big data if the practical database and useful software are introduced in time. The Journal of Big Data Research (JBR) supplies an efficient and open access publishing platform for big data research. The first issue of JBR aims to foster the dissemination of high-quality big data studies in the biological, medical and chemical database as well as the new algorithm and software for big data processing. The database and computing framework are selected to introduce the development of big data in the biological, medicine and drug discovery. The mature and functional database can be serviced in big data research of scientific fields. It promotes the scientists to extract the useful and essential dataset from the massive data. The grid computing and cloud computing supplies a new paradigm that offers an effective framework of computing and services. The research papers are welcomed from the scopes of the practical database, new algorithm and software for big data studies. All these kinds of papers not only provide the effective application methods and platforms, but also give a good promising future for big data research.
Mar 2018
Liu XiaopingCorresponding author
School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China
An important use of proteomics data from Mass Spectrometry (MS) is the classification of tumor types with respect to peptides in specific cancer types. It is highly critical to find an optimal set of markers among specific cancer peptides whose expression can be clinically utilized to build assays for the diagnosis or to track the progression of specific cancer types. A number of feature selection algorithms have been proposed to obtain the classification of MS data. In this article, we proposed an improved feature selection algorithm based on feature weighting. Relief algorithm can calculate the weight of different features according to the correlation between their characteristics and categories. F-score is a simple filter-based feature selection method by evaluating how two sets of real numbers discriminate from each other. The main goal of this paper is to introduce a new feature weighting selection algorithm combining score from f-value and weight from relief, which is more accurate when classifying high-resolution MALDI-TOF (matrix-assisted laser desorption and ionization time-of-flight) MS data. We have developed a four-step strategy for data processing based on: (1) Align the study sets by binning of raw MS data, (2) local maximum search(LMS) peak detection, (3) a new combination feature weighting selection algorithm and (4) support vector machines achieve a satisfactory performance of identifying cancer and the healthy. The best parameter set for LMS were achieved with control variable method, which achieve an average accuracy of 97.4167% (sd = 0.0146) and the best accuracy of 98.6111% in 1000 independent 10 -fold cross validations.
Nov 2017 DOI 10.14302/issn.2324-7339.jcrhap-17-1679
Tesfahuneygn GebrehiwetCorresponding author
Tigray health research institute, Mekelle
Background: Point-of-care diagnostic tests (POCTs) are increasingly used in both developing and developed countries. They allow same day testing and treatment at remote locations where no laboratory support is available. Quality control measures, which are routinely used in laboratories, have not been widely implemented for POCTs. This aimed to assess the integrity of the entire laboratory testing process, and aims to educate and improve performance in quality of HIV rapid testing. Methods: A health facility based cross section study was conducted from April to June 2016.Randomly selected health facilities were participated in the external quality assessment. Onsite evaluation and panel test were used to collect data using structured checklists and formats. Data was entered and analyzed using SPSS version 16. Results: Between April to June 2016, a total of 60 health facilities (145 testing points) from governmental health facilities (hospitals and health centers) were participated in the study. Among the participated testing points 41% have no designated area, 40% have no clean water for hand washing and 51% have no national test algorithm. The average performance of testing points was varies from 89.6% to 99.1% (Laboratory 99.1%, ANC 90.4%, TB clinic 91.4% and VCT 89.6%). In a multivariable logistic regression model, didn’t follow national testing algorithm to report client test results have statistical significance. Conclusions: High quality test results underpin accurate diagnosis and appropriate treatment for patients. But in the study area the score of proficiency testing result and coverage of training is slightly low comparing to other findings. Therefore following national testing algorithm to report client test results, training and monitoring are critical points to improve the proficiency testing score of testing points.
Feb 2016 DOI 10.14302/issn.2575-7881.jdrr-15-849
BOULILA MoncefCorresponding author
Professor, Université de Sfax- Institut de l’Olivier- B.P. 14, 4061 Sousse Ibn Khaldoun, Tunisia.
Reverse Transcription Polymerase Chain Reaction (RT-PCR) using new designed primers pair for Heat Shock Protein70 homologue (HSP70h) of Olive leaf yellowing-associated virus revealed 667 amplified product of 10 olive accessions collected from various olive-growing regions in Tunisia. Amplicons were cloned and sequenced. The sequences were deposited in the international databases. Pairwise sequence comparisons among 10 Tunisian isolates along with a reference sequence (AJ440010) extracted from GenBank revealed a nucleotide identity of 86.06-99.40 and an amino acid similarity of 91.89-99.55. Sequence multiple alignments were searched for evidence of recombination using three methods, ie. Differences of Sums of Squares (DSS) implemented in TOPALi v2.5 software and Single Breakpoint (SBP) along with GARD, a genetic algorithm, both incorporated in HyPhy package. All used methods pointed out the presence of putative breaking points in partially sequenced HSP70h-coding gene. Since failing to account for recombination can mislead the phylogeny inference and can elevate the false positive error rate in positive selection assessment, the use of GARD resulted in the reconstruction of different phylogenies on the left as well as on the right sides of putative recombination breaking points, and the 11 accessions were distributed into at least three clusters compared to MEGA6 software which delineated only two clades. Nonetheless, by dividing the aligned sequences at breakpoints into separate sequence sets, MEGA6 delineated a clustering pattern different from the former two. As a result, recombination reshuffled the affiliation of the different accessions to the clusters. Analysis of selection pressures exerted on HSP70h encoded protein using different models (SLAC, IFEL, FEL, REL, PARRIS, FUBAR, MEME, GA Branch, and PRIME) taking into account recombination, and implemented in HyPhy package, revealed that it underwent predominantly purifying selection as confirmed by Tajima’s D, Fu and Li’s D and F tests, and SNAP algorithm. However, a few sites were also under positive selection as assessed by various models such as FEL, IFEL, REL, MEME, and PRIME.